The present invention relates generally to quality management in a data-processing environment. Specifically, it relates to operational risk estimation and control associated with a data processing operation.
Errors in documents during a data processing operation, for example, data entry and data transformation are common. These errors may result in significant losses to an organization, especially if a large amount of data is processed. It is therefore important to control the quality of documents. Conventional techniques for controlling the quality of documents include error detection and correction, and determination of parameters for measuring errors. One such measurement parameter can be the percentage of documents with errors. However, these parameters do not directly indicate the impact of the errors to the organization.
Further, the conventional techniques for error detection are manual in nature. Errors can be detected by manually checking a set of documents to catch errors and compute the error rate. However, this technique may be error prone since the errors are detected manually. Further, the number of documents to be reviewed for catching errors (rather than just estimating error rates) is a function of the error rate. If the error rate is high, then a high percentage of documents need to be reviewed for catching a higher percentage of errors. Consequently, this technique can be labor intensive and therefore expensive.
Another technique for error prevention involves double typing the same document. The two different versions of the same document are compared electronically, and any discrepancies are reviewed and corrected. However, in this case each document needs to be double typed, which can be a labor-intensive exercise. The double typing and the confirmation of its correctness are done on a larger set of the documents. Further, a supervisor has to manually review each discrepancy to detect which of the two operators has made an error, or to correct the errors. Further, manual reviews themselves are prone to errors and result in wastage of labor, money and time. Conventional techniques for detection of errors and correction are therefore cumbersome and expensive.
Furthermore, data entry operators can become aware as to when the supervisors are carrying out quality checks, and concentrate on quality for that period. If the process requires double entry of a complete document, it may result in ‘gaming’ of the system by the data entry operators, i.e., they may be lax in the initial data entry and catch errors if there is a discrepancy.
In other conventional techniques, critical fields are pre-defined by a supervisor/management. These critical fields are defined on the basis of their subjective criticality. Subsequently, preventive and corrective measures are taken in these critical fields. Further these critical fields themselves are not updated automatically and are only updated periodically during management review. As a result, the quality of the processed document may not be improved beyond a certain extent.
Accordingly, there is a need for developing techniques that manage the quality of documents. Such techniques should be cost-effective, scalable, and less time-consuming. There is a need for techniques that can measure error rate, control error rate, predict errors, and enable their subsequent prevention. Further, there is a need for techniques that ensure that the critical fields are identified dynamically and automatically.
Further, these techniques should enable benchmarking of organizations, i.e., how well organizations control data processing operational risk relative to one another. Such a benchmark should be comparable across process variations, organization size, document type, etc. Also, measurement schemes for data processing operators and systems should be directly correlated to measures used to evaluate the organizations. This enables true alignment of measurement schemes with performance requirements. These techniques should also deter ‘gaming’ of the system by data entry operators and supervisors.